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Genetic Algorithm Wavelet Design for Signal Classification
August 2001 (vol. 23 no. 8)
pp. 890-895

Abstract—Biorthogonal wavelets are applied to parse multiaspect transient scattering data in the context of signal classification. A language-based genetic algorithm is used to design wavelet filters that enhance classification performance. The biorthogonal wavelets are implemented via the lifting procedure and the optimization is carried out using a classification-based cost function. Example results are presented for target classification using measured scattering data.

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Index Terms:
Genetic algorithms, wavelets, classification.
Citation:
Eric Jones, Paul Runkle, Nilanjan Dasgupta, Luise Couchman, Lawrence Carin, "Genetic Algorithm Wavelet Design for Signal Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 8, pp. 890-895, Aug. 2001, doi:10.1109/34.946991
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